Quali.com provides a “Cloud Sandbox for Development and Test Labs” which is said to “transform Dev and Test Labs into global, on-demand, lab-as-a-service clouds to maximize cost efficiencies, achieve faster releases, and ensure higher quality”.
Iterate.ai is a “self-service Innovation Platform” with access to plural Startups (which) which includes AI-based search, and “monitors” IoT, Deep Learning, App-less Mobility solutions and Virtual POS.
OpenLegacy.com allows integrating back-end systems of record through APIs to various systems of engagement such as mobile or web, striving to deliver secure managed cloud services without re-hosting or changing current applications or platforms.
Applaud.com provides functional testing services. End users may upload or direct to their website or app, indicate the type of testing and testing scope they seek, and obtain testing results for websites, apps, and connected devices. An end-user can receive and review issues in real time.
Wikipedia describes that Apache Flink is a community-driven framework for distributed big data analytics, like Hadoop and Spark. The core of Apache Flink is a distributed streaming dataflow engine written in Java and Scala Flink aims to bridge the gap between mapreduce-like systems and shared-nothing parallel database systems by executing arbitrary dataflow programs in a data-parallel and pipelined manner Flink's pipelined runtime system enables execution of bulk/batch and stream processing programs.
Wikipedia describes that mapreduce is a framework for processing parallelizable problems across large datasets using a large number of computers (nodes), collectively referred to as a cluster (if all nodes are on the same local network and use similar hardware) or as a grid (if the nodes are shared across geographically and administratively distributed systems, and use more heterogenous hardware). Processing can occur on data stored either in a filesystem (unstructured) or in a database (structured). Mapreduce can take advantage of data locality by processing data near where the data is stored to reduce the distance over which data is transmitted. In mapreduce, in an initial “Map” operation, each worker node applies the “map( )” function to the local data, and writes the output to a temporary storage. A master node ensures that only one copy of redundant input data is processed. In an interim “Shuffle” operation, Worker nodes redistribute data based on the output keys (produced by the “map( )” function), such that all data belonging to one key is located on the same worker node. In a final “Reduce” operation, worker nodes process each group of output data, per key, in parallel.
Wikipedia describes that mapreduce supports distributed processing of map and reduction operations. If each mapping operation is independent of others, all maps can be performed in parallel, limited by the number of independent data sources and/or number of CPUs near each source. Also, a set of ‘reducers’ can perform reduction, provided all outputs of the map operation that share the same key are presented to the same reducer at the same time, or providing that the reduction function is associative. Mapreduce can be applied to significantly larger datasets than “commodity” servers can handle; a large server farm using mapreduce can sort a petabyte of data in only a few hours. The parallelism is also advantageous because if one mapper or reducer fails, the work can be rescheduled if the input data is still available.
Wikipedia describes that mapreduce may include a 5-stage parallel and distributed computation which may run in sequence or the stages may be interleaved:    1. Prepare the Map( ) input—the “mapreduce system” designates Map processors, assigns the input key value K1 that each processor would work on, and provides that processor with all the input data associated with that key value.    2. Run the user-provided Map( ) code—Map( ) is run exactly once for each K1 key value, generating output organized by key values K2.    3. “Shuffle” the Map output to the Reduce processors—the mapreduce system designates Reduce processors, assigns the K2 key value each processor may work on, and provides that processor with all the Map-generated data associated with that key value.    4. Run the user-provided Reduce( ) code—Reduce( ) is run exactly once for each K2 key value produced by the Map stage.    5. Produce the final output—the mapreduce system collects all the Reduce output, and sorts it by K2 to produce the final outcome.
The disclosures of all publications and patent documents mentioned in the specification, and of the publications and patent documents cited therein directly or indirectly, are hereby incorporated by reference.